arXiv subjects

Steven Hancock

Publications and source records attributed to Steven Hancock.

10 recordsLinked to original sources

arXiv2026

Optimizing GEDI Simulator Configuration for European Temperate Forests

Accurate estimation of aboveground biomass density is essential for quantifying forest carbon stocks. NASA's GEDI mission provides valuable canopy structure data, but its sparse sampling necessitates the use of simulators to calibrate biomass models at field inventory locations. The widely used simulator of Hancock et al. (2019) emulates GEDI waveforms from airborne LiDAR point clouds, yet it has never been validated over European temperate forests. Here, we compare approximately 9,500 pairs of observed and simulated GEDI relative height (RH) profiles across French forests using the national airborne LiDAR program as input. We separate two sources of error: waveform modeling differences, assessed by referencing both simulated and real RH metrics to a common ALS-derived ground elevation, and ground detection bias, evaluated by comparing each GEDI L2A processing algorithm against the ALS reference. Under the baseline configuration, the mean absolute bias across the full RH profile reaches 0.69 m in leaf-on and 1.28 m in leaf-off conditions. Switching to intensity-based return weighting and selecting the a3 L2A algorithm reduces these biases to 0.44 m and 0.40 m respectively. The a3 algorithm also achieves near-unbiased ground detection (-0.03 m versus -0.88 m for the default), directly reducing a previously overlooked source of error. We also show that leaf-off acquisitions and low-sensitivity shots, both typically excluded from standard biomass products, are simulated as reliably as their counterparts, substantially expanding the potential calibration and inference datasets.

eess.SP↗

arXiv2026

StruMPL: Multi-task Dense Regression under Disjoint Partial Supervision and MNAR Labels

Estimating forest aboveground biomass (AGB) from Earth observation combines two structurally incompatible label sources: spaceborne lidar provides canopy structure at millions of locations but no biomass estimate, and ground-based plots provide biomass at thousands of biased locations but no metrics of structure. No single training sample carries labels for all target variables, plot labels are missing not at random (MNAR), and biomass is linked to the structural variables by known but biome-specific allometric laws. We formalise this as multi-task dense regression under heterogeneous disjoint partial supervision with MNAR labels and inter-task physical constraints, and propose StruMPL to address it jointly. A shared encoder feeds per-variable regression, imputation, and propensity heads for spatial MNAR correction, and a learnable physics module that evaluates the inter-task constraint on the model's own predictions at every pixel. The supervised loss uses an Augmented IPW (AIPW) pseudo-outcome with stop-gradients on the propensity and on the imputation baseline; we show analytically and empirically that both are necessary for joint optimisation to recover IPW-weighted stationary points while keeping the loss bounded. On two ecologically distinct biomes, StruMPL outperforms ablation variants and the closest published method on AGB RMSE and bias, with a stratified analysis showing AIPW reduces high-AGB bias by ~54%.

cs.CV↗

arXiv2026

Process-Guided Concept Bottleneck Model

Concept Bottleneck Models (CBMs) improve the explainability of black-box Deep Learning (DL) by introducing intermediate semantic concepts. However, standard CBMs often overlook domain-specific relationships and causal mechanisms, and their dependence on complete concept labels limits applicability in scientific domains where supervision is sparse but processes are well defined. To address this, we propose the Process-Guided Concept Bottleneck Model (PG-CBM), an extension of CBMs which constrains learning to follow domain-defined causal mechanisms through biophysically meaningful intermediate concepts. Using above ground biomass density estimation from Earth Observation data as a case study, we show that PG-CBM reduces error and bias compared to multiple benchmarks, whilst leveraging multi-source heterogeneous training data and producing interpretable intermediate outputs. Beyond improved accuracy, PG-CBM enhances transparency, enables detection of spurious learning, and provides scientific insights, representing a step toward more trustworthy AI systems in scientific applications.

cs.LG↗

arXiv2024

Comparing remote sensing-based forest biomass mapping approaches using new forest inventory plots in contrasting forests in northeastern and southwestern China

Large-scale high spatial resolution aboveground biomass (AGB) maps play a crucial role in determining forest carbon stocks and how they are changing, which is instrumental in understanding the global carbon cycle, and implementing policy to mitigate climate change. The advent of the new space-borne LiDAR sensor, NASA's GEDI instrument, provides unparalleled possibilities for the accurate and unbiased estimation of forest AGB at high resolution, particularly in dense and tall forests, where Synthetic Aperture Radar (SAR) and passive optical data exhibit saturation. However, GEDI is a sampling instrument, collecting dispersed footprints, and its data must be combined with that from other continuous cover satellites to create high-resolution maps, using local machine learning methods. In this study, we developed local models to estimate forest AGB from GEDI L2A data, as the models used to create GEDI L4 AGB data incorporated minimal field data from China. We then applied LightGBM and random forest regression to generate wall-to-wall AGB maps at 25 m resolution, using extensive GEDI footprints as well as Sentinel-1 data, ALOS-2 PALSAR-2 and Sentinel-2 optical data. Through a 5-fold cross-validation, LightGBM demonstrated a slightly better performance than Random Forest across two contrasting regions. However, in both regions, the computation speed of LightGBM is substantially faster than that of the random forest model, requiring roughly one-third of the time to compute on the same hardware. Through the validation against field data, the 25 m resolution AGB maps generated using the local models developed in this study exhibited higher accuracy compared to the GEDI L4B AGB data. We found in both regions an increase in error as slope increased. The trained models were tested on nearby but different regions and exhibited good performance.

cs.CV↗

arXiv2023

Forest aboveground biomass estimation using GEDI and earth observation data through attention-based deep learning

Accurate quantification of forest aboveground biomass (AGB) is critical for understanding carbon accounting in the context of climate change. In this study, we presented a novel attention-based deep learning approach for forest AGB estimation, primarily utilizing openly accessible EO data, including: GEDI LiDAR data, C-band Sentinel-1 SAR data, ALOS-2 PALSAR-2 data, and Sentinel-2 multispectral data. The attention UNet (AU) model achieved markedly higher accuracy for biomass estimation compared to the conventional RF algorithm. Specifically, the AU model attained an R2 of 0.66, RMSE of 43.66 Mg ha-1, and bias of 0.14 Mg ha-1, while RF resulted in lower scores of R2 0.62, RMSE 45.87 Mg ha-1, and bias 1.09 Mg ha-1. However, the superiority of the deep learning approach was not uniformly observed across all tested models. ResNet101 only achieved an R2 of 0.50, an RMSE of 52.93 Mg ha-1, and a bias of 0.99 Mg ha-1, while the UNet reported an R2 of 0.65, an RMSE of 44.28 Mg ha-1, and a substantial bias of 1.84 Mg ha-1. Moreover, to explore the performance of AU in the absence of spatial information, fully connected (FC) layers were employed to eliminate spatial information from the remote sensing data. AU-FC achieved intermediate R2 of 0.64, RMSE of 44.92 Mgha-1, and bias of -0.56 Mg ha-1, outperforming RF but underperforming AU model using spatial information. We also generated 10m forest AGB maps across Guangdong for the year 2019 using AU and compared it with that produced by RF. The AGB distributions from both models showed strong agreement with similar mean values; the mean forest AGB estimated by AU was 102.18 Mg ha-1 while that of RF was 104.84 Mg ha-1. Additionally, it was observed that the AGB map generated by AU provided superior spatial information. Overall, this research substantiates the feasibility of employing deep learning for biomass estimation based on satellite data.

cs.CV↗

arXiv2023

Infrared Nanoimaging of Hydrogenated Perovskite Nickelate Synaptic Devices

Solid-state devices made from correlated oxides such as perovskite nickelates are promising for neuromorphic computing by mimicking biological synaptic function. However, comprehending dopant action at the nanoscale poses a formidable challenge to understanding the elementary mechanisms involved. Here, we perform operando infrared nanoimaging of hydrogen-doped correlated perovskite, neodymium nickel oxide (H-NdNiO3) devices and reveal how an applied field perturbs dopant distribution at the nanoscale. This perturbation leads to stripe phases of varying conductivity perpendicular to the applied field, which define the macroscale electrical characteristics of the devices. Hyperspectral nano-FTIR imaging in conjunction with density functional theory calculations unveil a real-space map of multiple vibrational states of H-NNO associated with OH stretching modes and their dependence on the dopant concentration. Moreover, the localization of excess charges induces an out-of-plane lattice expansion in NNO which was confirmed by in-situ - x-ray diffraction and creates a strain that acts as a barrier against further diffusion. Our results and the techniques presented here hold great potential to the rapidly growing field of memristors and neuromorphic devices wherein nanoscale ion motion is fundamentally responsible for function.

physics.app-ph↗

arXiv2021

Quantitative Parametric Mapping of Tissues Properties from Standard Magnetic Resonance Imaging Enabled by Deep Learning

Magnetic resonance imaging (MRI) offers superior soft tissue contrast and is widely used in biomedicine. However, conventional MRI is not quantitative, which presents a bottleneck in image analysis and digital healthcare. Typically, additional scans are required to disentangle the effect of multiple parameters of MR and extract quantitative tissue properties. Here we investigate a data-driven strategy Q^2 MRI (Qualitative and Quantitative MRI) to derive quantitative parametric maps from standard MR images without additional data acquisition. By taking advantage of the interdependency between various MRI parametric maps buried in training data, the proposed deep learning strategy enables accurate prediction of tissue relaxation properties as well as other biophysical and biochemical characteristics from a single or a few images with conventional T_1/T_2 weighting. Superior performance has been achieved in quantitative MR imaging of the knee and liver. Q^2 MRI promises to provide a powerful tool for a variety of biomedical applications and facilitate the next generation of digital medicine.

physics.med-ph↗

arXiv2019

Arbitrarily large iterative tomographic reconstruction on multiple GPUs using the TIGRE toolbox

Tomographic image sizes keep increasing over time and while the GPUs that compute the tomographic reconstruction are also increasing in memory size, they are not doing so fast enough to reconstruct the largest datasets. This problem is often solved by reconstructing data in large clusters of GPUs with enough devices to fit the measured X-ray projections and reconstructed volume. Often this requires tens of GPUs, which is a very economically expensive solution. Access to single-node machines designed to reconstruct using just one or a few GPUs is more common in the field, but current software does not allow iterative reconstruction of volumes that do not fit in those GPUs. In this work, we propose a strategy to execute efficiently the required operations for iterative reconstruction for arbitrarily large images with any number of GPUs with arbitrarily small memories in a single node. Strategies for both the forward and backprojection operators are presented, along with two regularization approaches that are easily generalized to other projection types or regularizers. The proposed improvement also accelerates reconstruction of smaller images on single or multiple GPUs, providing faster code for time-critical medical applications. The resulting algorithm has been added to the TIGRE toolbox, a repository for iterative reconstruction algorithms for general CT, but this memory-saving and problem-splitting strategy can be easily adapted for any other GPU-based CT code.

cs.DC↗

arXiv2016

Flat Bunches with a Hollow Distribution for Space Charge Mitigation

Longitudinally hollow bunches provide one means to mitigate the impact of transverse space charge. The hollow distributions are created via dipolar parametric excitation during acceleration in CERN's Proton Synchrotron Booster. We present simulation work and beam measurements. Particular emphasis is given to the alleviation of space charge effects on the long injection plateau of the downstream Proton Synchrotron machine, which is the main goal of this study.

physics.acc-ph↗

arXiv2013

Fifty years of the CERN Proton Synchrotron : Volume 2

This report sums up in two volumes the first 50 years of operation of the CERN Proton Synchrotron. After an introduction on the genesis of the machine, and a description of its magnet and powering systems, the first volume focuses on some of the many innovations in accelerator physics and instrumentation that it has pioneered, such as transition crossing, RF gymnastics, extractions, phase space tomography, or transverse emittance measurement by wire scanners. The second volume describes the other machines in the PS complex: the proton linear accelerators, the PS Booster, the LEP pre-injector, the heavy-ion linac and accumulator, and the antiproton rings.

physics.acc-ph↗